# Blame Shifting in Presidential Systems: Ministerial Terminations' Corrective Effect on Approval

## Overview

This repository contains the replication files, data and codebook of the paper "Blame Shifting in Presidential Systems: Ministerial Terminations' Corrective Effect on Approval" accepted for publication in *Public Opinion Quarterly* (POQ):

> How do ministerial terminations affect presidential approval? Presidents face unexpected challenges related to stochastic events such as scandals, policy failures or economic crises. We argue that the termination of ministers who have received calls for their resignation presents an opportunity for the president to send signals to the electorate in the expectation of a corrective effect on popularity through a blame-shifting dynamic. The central argument is that this dynamic only occurs in coalition governments where political responsibility may be more easily attributed to the coalition’s different parties and factions, weakening personalisation centred on the president and facilitating blame-shifting and the corrective effect. The expectation of a corrective effect on approval is tested using instrumental variables (IV) regressions applied to novel data on ministerial terminations and resignation calls in 124 governments in 12 presidential democracies. The data were gathered by combining data mining, machine learning techniques and survey marginal time series based on the dyad ratios algorithm for approval. The main findings support the expectation that individual terminations of tainted ministers generate a corrective effect of nine points on presidential approval in coalition governments, which decreases in the medium and long term.

## Metadata and Preservation

This repository has been created with Git version control and deposited in Harvard Dataverse: https://dataverse.harvard.edu/dataverse/poq

Below is a brief description of each file in this repository:

- **Blame_Shifting_Codebook.pdf**. Describes how the dataset in `blame_shifting_dataset.csv` was constructed and defines each variable.

- **blame_shifting_dataset.csv**. A UTF-8 CSV file containing quarterly observations of presidential cabinets in several Latin American countries over approximately 50 years. It documents ministerial resignations/dismissals, cabinet composition (e.g., average age, party affiliation), macroeconomic indicators, and presidential approval measures, with the aim of examining how firing “tainted” ministers can shift blame and affect presidential approval.

- **figure_1.png**.  A figure illustrating the marginal treatment effects (MTE) of dismissing tainted ministers on presidential approval, generated by the `stage_2_mte_approval.R` script in 600 DPI.

- **ivreg_0.6-1.tar.gz**. Source tarball for R packages used in the replication. It is included to ensure reproducibility and version control.

- **lpSolveAPI_5.5.2.0-17.12.tar.gz**. Source tarball for R packages used in the replication. It is included to ensure reproducibility and version control.

- **performance_0.10.2.tar.gz**. Source tarball for R packages used in the replication. It is included to ensure reproducibility and version control.

- **README.md**. The main overview of this repository provides metadata, preservation information and instructions for replicating the analyses.

- **renv.lock**. The renv lock file that specifies exact package versions used in this project, ensuring a consistent computational environment for replication.

- **stage_1_iv_models.R**. R script that estimates the effect of dismissing tainted ministers on presidential approval using instrumental variables (IV). It also generates the HTML output `table_1.html`.

- **stage_2_mte_approval.R**. R script that calculates the marginal treatment effects (MTE) of dismissing tainted ministers on presidential approval, and produces `figure_1.png`.

- **table_1.html**. The HTML results table (IV estimates) created by the `stage_1_iv_models.R` script.

## Getting Started

The original code, written in 2023, used `R v4.2.2 -- Innocent and Trusting` and `R v4.3.3 -- Angel Food Cake`.

The scripts deposited in Harvard Dataverse were rerun in April 2025 using `R v4.5.0 -- How About a Twenty-Six`. It is important to note that `renv.lock` is the project's lock file, and all installed and required R packages are recorded there. The revision in August 2025 was due to a slight change in the article's title.

The following files should be run in order to replicate the main results of the paper:

- `stage_1_iv_models.R`
- `stage_2_mte_approval.R`

Some package versions are particularly relevant and are included in the repository and are indicated in the code as follows:

``` R
install.packages("ivreg_0.6-1.tar.gz", repos = NULL, type = "source")
install.packages("performance_0.10.2.tar.gz", repos = NULL, type = "source")
install.packages("lpSolveAPI_5.5.2.0-17.12.tar.gz", repos = NULL, type = "source")
```

## Author

Bastián González-Bustamante \
Leiden University \
Universidad Diego Portales \
[ORCID iD 0000-0003-1510-6820](https://orcid.org/0000-0003-1510-6820) \
https://bgonzalezbustamante.com

### Latest Revision

August 16, 2025.
